mcp server

benchmark-broccoli

interesting

Shriya-Chauhan/benchmark-broccolicommunitylistedunreviewed

Use tool

Set up

Runs on your computer. Pick your agent and operating system.

Before you start

Node.js 20 or newer

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "benchmark-broccoli": {
      "command": "npx",
      "args": [
        "-y",
        "benchmark-broccoli"
      ]
    }
  }
}

same format Cursor, GitHub Copilot CLI, Junie, JetBrains AI Assistant, Augment (Auggie CLI), Factory Droid, Kiro, Devin CLI +12

Values in angle brackets are placeholders. Restart Claude Desktop after editing its config.Claude Desktop docs

README

synced Oct 27, 2026

benchmark-broccoli

A transparent Model Context Protocol (MCP) proxy that sits between your AI client and any MCP server, measuring tokens, estimated cost, and latency for every tool call. Metrics stream to a live dashboard with per-query session tracking and multi-model cost comparison.

Features

Works with any MCP server — filesystem, database, GraphQL, custom servers
Works with any AI client — Cursor, VS Code, OpenCode, Claude Desktop, custom agents
Per-query session tracking — groups tool calls by prompt/query with configurable time gap
Schema overhead attribution — full listTools() payload token count, booked once per proxy process (first tool call)
Per-user cost tracking — MCP_USER in repo .env; shown per call on the dashboard
Multi-model cost comparison — switch models in the dashboard and see costs recalculated live
Accurate tokenization — uses tiktoken (o200k_base) with char-length fallback
Live dashboard — auto-updating via Server-Sent Events, dark theme, sessions view, per-tool breakdown
Export — download call history as CSV for external analysis
JSONL log — append-only calls.jsonl for downstream analysis

Quick Start

1. Install

git clone https://github.com/Shriya-Chauhan/benchmark-broccoli.git
cd benchmark-broccoli
npm install

Optional: copy .env.example to .env and adjust COST_MODEL, MCP_USER, ports, etc.

2. Configure your AI client

Point your client's MCP config at the proxy, passing the real server command after --.

Cursor (~/.cursor/mcp.json):

{
  "mcpServers": {
    "my-server": {
      "command": "npx",
      "args": [
        "tsx", "/absolute/path/to/benchmark-broccoli/src/index.ts",
        "--",
        "npx", "-y", "mcp-remote", "https://my-mcp-server.example.com/mcp"
      ],
      "env": {
        "COST_MODEL": "claude-sonnet-4-20250514"
      }
    }
  }
}

3. Start the dashboard

npm start
# → [dashboard] http://127.0.0.1:3000

Open http://127.0.0.1:3000 in your browser. Use your AI client normally — every tool call appears on the dashboard in real time, grouped by session.

Set MCP_USER in .env (repo root); restart the MCP server in your client after you change it.

Sessions

The proxy automatically groups tool calls into sessions using a time-gap heuristic. When your AI client processes a prompt, it typically makes several tool calls in quick succession. If no call arrives within SESSION_GAP_MS (default 30 seconds), the next call starts a new session.

Each session tracks:

Schema overhead — token cost of the full listTools() payload, attributed on the first tool call after the proxy starts only (not repeated when a new session begins after an idle gap)
Call sequence — numbered tool calls within the session
Client name — filled when the SDK exposes the host clientInfo (often empty; not reliable)
User — whatever MCP_USER was when that call was recorded (usually from .env)
Aggregated cost — total cost across all calls in the session

The dashboard shows sessions as collapsible cards. Click to expand and see individual calls.

Built-in Model Pricing

Model	Input / 1M	Output / 1M
claude-sonnet-4-20250514	$3.00	$15.00
claude-opus-4-20250514	$15.00	$75.00
claude-3-5-sonnet-20241022	$3.00	$15.00
claude-3-5-haiku-20241022	$0.80	$4.00
gpt-4o	$2.50	$10.00
gpt-4o-mini	$0.15	$0.60
gpt-4.1	$2.00	$8.00
gpt-4.1-mini	$0.40	$1.60
gpt-4.1-nano	$0.10	$0.40
gemini-2.5-pro	$1.25	$10.00
gemini-2.5-flash	$0.15	$0.60

Repository layout

Path	Role
`src/index.ts`	MCP stdio proxy → upstream child; writes `calls.jsonl`
`src/dashboard.ts`	Express app + SSE; reads same JSONL
`src/store.ts`	JSONL path, append/load/clear/delete session
`src/cost.ts`	Model pricing + `estimateCost`
`src/tokenizer.ts`	tiktoken / fallback counting
`src/load-repo-env.ts`	Optional repo `.env` (import first from entrypoints)
`src/public/index.html`	Single-page dashboard (vanilla JS)

Contributing

Contributions are welcome! Please open an issue or pull request.

Fork the repo
Create a feature branch (git checkout -b feature/my-change)
Run npm test and npm run typecheck
Push and open a PR

benchmark-broccoli

Set up

README

benchmark-broccoli

Features

Quick Start

1. Install

2. Configure your AI client

3. Start the dashboard

Sessions

Built-in Model Pricing

Repository layout

Contributing

License